Why aren't individuals with developmental language disorders able to learn language as quickly or as well as their typical language peers? This question is at the heart of the study of developmental language disorders and, if understood, could provide a foundation to improve the language outcomes for affected individuals. In this grant, we propose to use functional magnetic resonance imaging (fMRI) to visualize neural resource allocation during the process of language learning by adults with typical language and with developmental language disorder. We will use paradigms that are known to produce rapid learning (over the course of minutes) so that the dynamics of language learning can be observed over the course of multiple fMRI scans within a single experimental session. The behavioral paradigms selected will focus on three aspects of language form, considered a core deficit for the disorder. We will use natural language stimuli (elements drawn from unfamiliar foreign languages) to enhance the ecological validity of the work. Advanced statistical modeling techniques, including Independent Component Analysis and Linear Structural Equation Modeling will be used to model fMRI data at a network level. Diffusion Tensor Imaging (DTI) will also serve to characterize the structural connections that underlie network-level interactions. This work will advance the field's basic understanding of how resources are allocated during the process of learning language and will address a current theory that posits language acquisition involves the allocation of both language and other cognitive resources in the service of learning. Furthermore, the work will help to determine whether learning by those with developmental language disorders reflects a general inability to allocate neural resources at sufficiently high levels (i.e., general hypoactivation) vs. ineffective allocation of resources (regional differences in activation or differences in connectivity among regions activated), or both.